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An intelligent decision-making system for assembly process planning based on machine learning considering the variety of assembly unit and assembly process

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Abstract

Current assembly process planning of complex products depends mainly on existing process templates and the experience of technical personnel, resulting in low design efficiency, poor process pertinence, low intelligence, and difficulty to extract the assembly knowledge. To address these problems, this paper proposes an intelligent decision-making system for complex products assembly process planning based on the machine learning (ML) method, providing a targeted decision-making scheme with an assembly process structure tree. The characteristics, variations, and similarities of the assembly process for complex products were analyzed. A hierarchical model of the product assembly process is established, based on which the assembly process decision-making for overall structure is decomposed into several units. Then, an intelligent decision-making model for new product assembly process planning was constructed through ML by considering the variability of component composition of different product models and existing product assembly data. Based on these, an optimized decision-making model was established through a swarm intelligence algorithm that automatically optimizes the initial weight, threshold, learning rate, and feedback process, improving the decision efficiency and accuracy of system. To validate the system, the intelligent decision-making assembly process was conducted on a cylinder cap as a case study, and the system capability is discussed in terms of ML performance and industrial applicability.

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Availability of data and material

The datasets used or analyzed during the current study are partial available from the corresponding author on reasonable request.

Code availability

The code is partial available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. School of Mechanical Engineer, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, China

    Sheng-Wen Zhang, Zhan Wang, De-Jun Cheng & Xi-Feng Fang

Authors
  1. Sheng-Wen Zhang
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  2. Zhan Wang
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  3. De-Jun Cheng
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  4. Xi-Feng Fang
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Contributions

All authors contributed to the study conception and design. Sheng-Wen Zhang: supervision, method design, review. Zhan Wang: method design, programming, data collection and analysis, writing - original draft. De-Jun Cheng: supervision, method design, review, editing. Xi-Feng Fang: method design, review.

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Correspondence to Sheng-Wen Zhang or De-Jun Cheng.

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Cite this article

Zhang, SW., Wang, Z., Cheng, DJ. et al. An intelligent decision-making system for assembly process planning based on machine learning considering the variety of assembly unit and assembly process. Int J Adv Manuf Technol 121, 805–825 (2022). https://doi.org/10.1007/s00170-022-09350-6

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  • DOI: https://doi.org/10.1007/s00170-022-09350-6

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